Determine Measurement Set for Parameter Estimation in Biological Systems Modelling
Parameter estimation is challenging for biological systems modelling since the model is normally of high dimension, the measurement data are sparse and noisy, and the cost of experiments is high. Accurate recovery of parameters depend on the quantity and quality of measurement data. It is therefore important to know what measurements to be taken, when and how through optimal experimental design (OED). In this paper we present a method to determine the most informative measurement set for parameter estimation of dynamic systems, in particular biochemical reaction systems, such that the unknown parameters can be inferred with the best possible statistical quality using the data collected from the designed experiments. System analysis using matrix theory is introduced to examine the number of necessary measurement variables. The priority of each measurement variable is determined by optimal experimental design based on Fisher information matrix (FIM). The applicability and advantages of the proposed method are illustrated through an example of a signal pathway model.
Measurement Set Selection Optimal Experimental Design Parameter Estimation Biological Systems
Hong Yue Jianfang Jia
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK School of Information and Communication Engineering, North University of China, Taiyuan 030051, P. R
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
7457-7462
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）